Advertisement

Reference architecture of common service platform for Industrial Big Data (I-BD) based on multi-party co-construction

  • Xianyu ZhangEmail author
  • Xinguo Ming
  • Dao Yin
ORIGINAL ARTICLE

Abstract

In today’s information age with rapid development, all kinds of data have exploded. The Industrial Big data Application (I-BD) is generated by the continuous infiltration of big data in industry. Scholars have done a lot of research on the method, technology, and architecture of industrial big data from the perspective of data flow. However, there are relatively few studies on the reference model, reference architecture, and implementation path for industrial big data from the perspectives of detailed application scenarios, common service platform, and specific implementation. In this paper, firstly, the current situation of I-BD is analyzed. Secondly, a general reference model for I-BD is proposed, which consists of Industry (I) dimension, Application scenario (A) dimension, and common service platform (P) dimension. Further, the overall planning of application scenarios for I-BD based on industrial value chain for I-BD is studied. Again, a reference architecture of common service platform for I-BD based on multi-party co-construction is proposed. Finally, the implementation path of common service platform for I-BD is given. It can be used as a reference for industry and government to design, set, and carry out I-BD.

Keywords

Big data Industrial big data Intelligent manufacturing Common service platform Application scenario Reference model 

Notes

Acknowledgments

The authors would like to thank Shanghai Key Laboratory of Advanced Manufacturing Environment, Shanghai Research Center for industrial Informatics (SRCI2), and SJTU Innovation Center of Producer Service Development (SICPSD) for the funding support to this research.

Funding

This work was supported by the National Natural Science Foundation of China [grant number 71632008] and Major Special Basic Research Projects for Aero engines and Gas turbines [grant number 2017-I-0007-0008, grant number 2017-I-0011-0012].

References

  1. 1.
    Fan J, Han F, Liu H (2014) Challenges of big data analysis. Natl Sci Rev 1(2):293–314 https://www.researchgate.net/publication/255687770_Challenges_of_Big_Data_Analysis Google Scholar
  2. 2.
    Bello-Orgaz G, Jung JJ, Camacho D (2016) Social big data: recent achievements and new challenges. Information Fusion 28:45–59 https://www.researchgate.net/publication/281412960 Google Scholar
  3. 3.
    Kuo YH, Kusiak A (2018) From data to big data in production research: the past and future trends. Int J Prod Res 11:1–26 https://www.researchgate.net/publication/323519636 Google Scholar
  4. 4.
    Li X, Li X (2018) Big data and its key Technology in the Future. Computing in Science & Engineering 20(4):75–88 https://www.researchgate.net/publication/326310354 Google Scholar
  5. 5.
    Gaff BM, Sussman HE, Geetter J (2014) Privacy and big data. Computer 47(6):7–9 https://www.researchgate.net/publication/263285388 Google Scholar
  6. 6.
    Bertot JC, 2013 Choi H big data and e-government:issues, policies, and recommendations. Conference: Proceedings of the 4th Annual International Conference on Digital Government Research https://www.researchgate.net/publication/262158521. Accessed 20 June 2019
  7. 7.
    Chen HM, Kazman R, Haziyev S (2016) Strategic prototyping for developing big data systems. IEEE Softw 33(2):36–43 http://www.researchgate.net/publication/296480105 Google Scholar
  8. 8.
    Kong X, Feng M, Wang R (2015) The current status and challenges of establishment and utilization of medical big data in China. European Geriatric Medicine 6(6):515–517 http://www.researchgate.net/publication/283907774 Google Scholar
  9. 9.
    Dubey R, Gunasekaran A, Childe SJ, Wamba SF, Papadopoulos T (2016) The impact of big data on world-class sustainable manufacturing. Int J Adv Manuf Technol 84(1–4):631–645.  https://doi.org/10.1007/s00170-015-7674-1 http://link.springer.com/article/10.1007/s00170-015-7674-1 CrossRefGoogle Scholar
  10. 10.
    Williams E, Moore J, Li SW, Rustici G, Tarkowska A, Chessel A, Leo S, Antal B, Ferguson RK, Sarkans U (2017) The image data resource: a bioimage data integration and publication platform. Nat Methods 14(8):775. http://www.researchgate.net/publication/318173201–781Google Scholar
  11. 11.
    Gijzen H (2013) Development: big data for a sustainable future. Nature 502(7469):38 http://www.researchgate.net/publication/257350114 Google Scholar
  12. 12.
    Ramakrishnan N, Kumar R (2016) Big Data. Computer 49(4):20–22 https://www.researchgate.net/publication/301306984 Google Scholar
  13. 13.
    Sezer OB, Dogdu E, Ozbayoglu AM (2017) Context aware computing, learning and big data in internet of things: a survey. IEEE Internet of Things Journal PP 99:1–1 https://www.researchgate.net/publication/321090552 Google Scholar
  14. 14.
    Wu D, Birge JR (2017) Risk intelligence in big data era: a review and introduction to special issue. IEEE Transactions on Cybernetics 46(8):1718–1720 https://www.researchgate.net/publication/305733389 Google Scholar
  15. 15.
    Joseph RC, Johnson NA (2013) Big data and transformational government. It Professional 15(6):43–48 https://www.researchgate.net/publication/260721212 Google Scholar
  16. 16.
    Xiang C, Fang L, Hong X, Yang L (2017) Exploiting Mobile big data: sources, features, and applications. IEEE Netw 31(1):72–79 http://www.researchgate.net/publication/312668677 Google Scholar
  17. 17.
    Qi Q, Fei T (2018) Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access 6(99):1–1 http://www.researchgate.net/publication/322512249 Google Scholar
  18. 18.
    Tseng C-M, Chau SC-K, Liu X (2017) Improving viability of electric taxis by taxi service strategy optimization: a big data study of new York City. IEEE Transactions on Intelligent Transportation Systems PP 99:1–13 https://www.researchgate.net/publication/320033276 Google Scholar
  19. 19.
    Shu Y, Liang M, Cheng F, Zhang Z, Zhao J (2016) Abnormal situation management: challenges and opportunities in the big data era. Comput Chem Eng 91:104–113 https://www.researchgate.net/publication/301352896 Google Scholar
  20. 20.
    Akter S, Wamba SF (2017) Big data and disaster management: a systematic review and agenda for future research. Ann Oper Res 9:1–21 https://www.researchgate.net/publication/319231257 Google Scholar
  21. 21.
    Chen X, Shuai S, Tian Z, Zhen X, Peng Y (2016) Impacts of air pollution and its spatial spillover effect on public health based on China's big data sample. J Clean Prod 142:915–925 https://www.researchgate.net/publication/297659435 Google Scholar
  22. 22.
    Woo J, Shin S-J, Seo W, Meilanitasari P (2018) Developing a big data analytics platform for manufacturing systems: architecture, method, and implementation. Int J Adv Manuf Technol 99(9–12):2193–2217.  https://doi.org/10.1007/s00170-018-2416-9 https://link.springer.com/article/10.1007/s00170-018-2416-9 CrossRefGoogle Scholar
  23. 23.
    Alves W, Martins D, Bezerra U, Klautau A (2017) A hybrid approach for big data outlier detection from electric power SCADA system. IEEE Lat Am Trans 15(1):57–64 http://www.researchgate.net/publication/312668624 Google Scholar
  24. 24.
    Draxl C, Scheffler M (2018) NOMAD: the FAIR concept for big-data-driven materials science. MRS Bull 43(09):676–682 http://www.researchgate.net/publication/325142892 Google Scholar
  25. 25.
    Li Z, Fei RY, Wang Y, Ning B, Tao T (2018) Big data analytics in intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 20(1):383–398 https://www.researchgate.net/publication/324712011 Google Scholar
  26. 26.
    Zhang X, Ming X, Liu Z, Qu Y, Yin D (2019) A framework and implementation of customer platform-connection manufactory to service (CPMS) model in product service system. J Clean Prod 230:798–819.  https://doi.org/10.1016/j.jclepro.2019.04.382 http://www.sciencedirect.com/science/article/pii/S0959652619314830 CrossRefGoogle Scholar
  27. 27.
    Zhang Y, Shan R, Yang L, Sakao T, Huisingh D (2017) A framework for big data driven product lifecycle management. J Clean Prod 159:229–240 https://www.researchgate.net/publication/316894948 Google Scholar
  28. 28.
    Zhu J, Yuan Y, Li D, Gao F (2018) Monitoring big process data of industrial plants with multiple operating modes based on Hadoop. J Taiwan Inst Chem Eng. 91:10–21 http://www.researchgate.net/publication/325601320 Google Scholar
  29. 29.
    Choi TM, Chan HK, Yue X (2016) Recent development in big data analytics for business operations and risk management. IEEE Transactions on Cybernetics 47(1):81–92 https://www.researchgate.net/publication/290442195 Google Scholar
  30. 30.
    Prasad S, Zakaria R, Altay N (2016) Big data in humanitarian supply chain networks: a resource dependence perspective. Ann Oper Res 270:1–31. http://link.springer.com/10.1007/s10479-016-2280-7 MathSciNetGoogle Scholar
  31. 31.
    Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F (2018) Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 94(9–12):3563–3576.  https://doi.org/10.1007/s00170-017-0233-1 https://link.springer.com/article/10.1007%2Fs00170-017-0233-1 CrossRefGoogle Scholar
  32. 32.
    Vagliano I, Günther F, Heinz M, Apaolaza A, Bienia I, Breitfuss G, Blume T, Collyda C, Fessl A, Gottfried S (2018) Open innovation in the big data era with the MOVING platform: an integrated working and training approach for data-savvy information professionals. IEEE MultiMedia PP 99:1–1 http://ieeexplore.ieee.org/document/8494800/ Google Scholar
  33. 33.
    Esposito C, Ficco M, Palmieri F, Castiglione A (2015) A knowledge-based platform for big data analytics based on publish/subscribe services and stream processing. Knowl-Based Syst 79(C):3–17 https://www.researchgate.net/publication/262342402 Google Scholar
  34. 34.
    See-To EWK, Ngai EWT (2016) Customer reviews for demand distribution and sales nowcasting: a big data approach. Ann Oper Res 6:1–17 http://link.springer.com/10.1007/s10479-016-2296-z Google Scholar
  35. 35.
    Zhan Y, Tan KH, Li Y, Ying KT (2016) Unlocking the power of big data in new product development. Ann Oper Res 270:1–19. http://link.springer.com/10.1007/s10479-016-2379-x Google Scholar
  36. 36.
    Chang A-F, Liu YA (2010) Integrated process modeling and product Design of Biodiesel Manufacturing. Ind Eng Chem Res 49(3):1197–1213.  https://doi.org/10.1021/ie9010047 https://www.researchgate.net/publication/231390821 CrossRefGoogle Scholar
  37. 37.
    Mishra D, Gunasekaran A, Papadopoulos T, Childe SJ (2016) Big data and supply chain management: a review and bibliometric analysis. Ann Oper Res 270:1–24. http://link.springer.com/10.1007/s10479-016-2236-y zbMATHGoogle Scholar
  38. 38.
    Kusiak A (2017) Smart manufacturing must embrace big data. Nature 544(7648):23–25 http://www.researchgate.net/publication/315792168 Google Scholar
  39. 39.
    Moniz S, Barbosa-Povoa AP, de Sousa JP, Duarte P (2014) Solution methodology for scheduling problems in batch plants. Ind Eng Chem Res 53(49):19265–19281.  https://doi.org/10.1021/ie403129y http://www.researchgate.net/publication/272784489 CrossRefGoogle Scholar
  40. 40.
    Xu X, Sheng QZ, Zhang LJ, Fan Y, Dustdar S (2015) From big data to big service. Computer 48(7):80–83 https://www.researchgate.net/publication/282544285 Google Scholar
  41. 41.
    Spiess J, T'Joens Y, Dragnea R, Spencer P, Philippart L (2014) Using big data to improve customer experience and business performance. Bell Labs Technical Journal 18(4):3–17 https://www.researchgate.net/publication/263128852 Google Scholar
  42. 42.
    Zhang X, Ming X, Liu Z, Qu Y, Yin D (2019) An overall framework and subsystems for smart manufacturing integrated system (SMIS) from multi-layers based on multi-perspectives. Int J Adv Manuf Technol 103:703–722.  https://doi.org/10.1007/s00170-019-03593-6 CrossRefGoogle Scholar
  43. 43.
    Tran QT, Nguyen SD, Seo TI (2019) Algorithm for Estimating Online Bearing Fault Upon the Ability to Extract Meaningful Information from Big Data of Intelligent Structures. IEEE Trans Ind Electron 66(5):3204–3813  https://doi.org/10.1109/TIE.2018.2847704 Google Scholar
  44. 44.
    Serb A, Bill J, Khiat A, Berdan R, Legenstein R, Prodromakis T (2016) Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses. Nat Commun 7(1261):12611 http://www.researchgate.net/publication/308755993 Google Scholar
  45. 45.
    Onel M, Kieslich CA, Guzman YA, Floudas CA, Pistikopoulos EN (2018) Big data approach to batch process monitoring: simultaneous fault detection and diagnosis using nonlinear support vector machine-based feature selection. Comput Chem Eng 115:46–63 https://www.researchgate.net/publication/324070122 Google Scholar
  46. 46.
    Kumar M, Rath NK, Rath SK (2016) Analysis of microarray leukemia data using an efficient MapReduce-based K-nearest-neighbor classifier. J Biomed Inform 60:395–409 https://www.researchgate.net/publication/297891757 Google Scholar
  47. 47.
    Lupiani E, Juarez JM, Palma J, Marin R (2017) Monitoring elderly people at home with temporal case-based reasoning. Knowl-Based Syst https://www.researchgate.net/publication/318594717 134:116–134Google Scholar
  48. 48.
    Li X, Song J, Huang B (2016) A scientific workflow management system architecture and its scheduling based on cloud service platform for manufacturing big data analytics. Int J Adv Manuf Technol 84(1–4):119–131.  https://doi.org/10.1007/s00170-015-7804-9 https://link.springer.com/article/10.1007/s00170-015-7804-9 CrossRefGoogle Scholar
  49. 49.
    Zhou Z, Gao C, Chen X, Yan Z, Mumtaz S, Rodriguez J (2018) Social big-data-based content dissemination in internet of vehicles. IEEE Transactions on Industrial Informatics 14(2):768–777 https://www.researchgate.net/publication/318766784 Google Scholar
  50. 50.
    He R, Bo A, Molisch AF, Stuber GL, Li Q, Zhong Z, Jian Y (2018) Clustering enabled Wireless Channel modeling using big data algorithms. IEEE Communications Magazine PP 99:1–7 https://www.researchgate.net/publication/322994586 Google Scholar
  51. 51.
    Zhang X, Ming X, Liu Z, Yin D, Chen Z, Chang Y (2019) A reference framework and overall planning of industrial artificial intelligence (I-AI) for new application scenarios. Int J Adv Manuf Technol 101(9–12):2367–2389.  https://doi.org/10.1007/s00170-018-3106-3 https://link.springer.com/article/10.1007/s00170-018-3106-3 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Smart Manufacturing, School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai CityChina

Personalised recommendations